Introduction To Large Language Models: Non-Technical Course
An overview of Large Language Models for non-technical people, less the jargons, more simple explanations.
Artificial Intelligence,Machine Learning
Lectures -23
Resources -1
Duration -2.5 hours
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Course Description
Large language models are a type of artificial intelligence that can read and write texts in natural languages, such as English, Chinese, or Spanish. They learn from a lot of text data, such as books, articles, tweets, and more public internet data.
Everyday, LLMs are evolving. New models are coming out and new practices are being introduced to optimize the way use this technology. I have made this course dynamic. Instead of just providing exact information, I also point you to external resources where you can get the latest updates.
In this course, you will discover what large language models are, how they function, and what they can accomplish. We will also discuss some of the advantages and disadvantages of using large language models for different purposes and applications. This course is intended for non-technical people who want to gain a basic understanding of large language models without diving into the technical details or jargons.
What you’ll learn
Examples of Large Language Models aside from ChatGPT.
How Large Language Models work.
How to execute different use cases using ChatGPT.
How to fine-tune a model with custom dataset.
What are the existing challenges of Large Language Models and Generative AI in general.
Resources you can use to further learn about Large Language Models.
By the end of this course, you will have a clearer grasp of large language models and their possible impact in your personal life and professional career.
Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction
1 Lectures
- Introduction 01:40 01:40
What is a Large Language Model?
3 Lectures
Important Terminologies
7 Lectures
How LLMs are trained
2 Lectures
What are some examples of Large Language Models?
2 Lectures
Different use cases of LLMs
4 Lectures
Finetuning an LLM using custom dataset
3 Lectures
End note
1 Lectures
Instructor Details
Leomar Osorio
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